Auto Loans Research Articles

On the interdependencies between mortgage, credit card and auto loans delinquency rates: evidence from the US states plus the District of Columbia

Purpose This study aims to explain how delinquency shocks in one type of debt contaminate the others. That is, the authors aim to shed light on the time pattern of delinquencies in different debt types. Design/methodology/approach This study analyzes the interdependencies between mortgage, credit card and auto loans delinquency rates in the USA from 2003 to 2019, using a panel VAR-X, the panel Granger causality tests and the Geweke linear dependence measures. The authors also compute the impulse response functions of a shock to one kind of debt on the others and decompose the variance of the forecast errors. Findings The authors find a statistically significant bidirectional Granger causality between the delinquencies. The Geweke measures of linear dependence and the Dumitrescu and Hurlin Granger non-causality tests support that mortgage predominantly causes credit card and auto loan delinquencies. Auto loans also cause credit card delinquencies. The impulse response functions confirm this pattern. This scenario aligns with a sequence where debtors consider rational first to default on credit cards, second on auto loans and only on mortgages in the last instance. Indeed, credit card delinquencies Granger-cause delinquencies in other debts when it occurs. Originality/value To the best of the authors’ knowledge, this is the first study to focus on the temporal pattern of delinquency rates for all the US states, using panel data. Furthermore, the results call for policymakers to design regulations to break the transmission channel from debt delinquencies.

Do ESG Investors care about carbon emissions? Evidence from securitized auto loans

Do ESG Investors care about carbon emissions? Evidence from securitized auto loans

Credit contagion risk in German auto loans

Credit contagion risk in German auto loans

Research on sales strategy of electric vehicle target customers based on machine learning algorithm

Due to the influence of various subjective and objective factors, there is great uncertainty in consumers' willingness to purchase new energy vehicles. In this study, it is hoped that based on customer satisfaction and personal characteristic information, the main factors affecting whether the target customers are willing to purchase new energy vehicles are explored, so that corresponding sales strategies can be formulated. In this study, two different embedding methods based on penalty terms and tree models are used for feature selection, the former using three models LR, LASSO and SVM, and the latter using RF and LightGBM models for a total of five models for machine learning to find out the relevant features that affect the sales of different brands, and the voting method is applied to the selected features, and the results are found. The battery technology performance of electric vehicles, comfort, annual mortgage of target customers and the ratio of auto loan to annual household income have a significant impact on the sales of all three brands. In addition, affordability, safety and customer's work situation also had different degrees of influence on sales of the different brands. A multilayer perceptron (BP neural network) prediction model is built for each brand's target customers to predict their purchase intention. The prediction accuracy of the network is improved by balancing the positive and negative samples so that the ratio of the two types of samples in the dataset is approximately 1:1. The test set is also used for real-time testing to determine the fit status of the dataset. The accuracy of the final model was above 80% on both the training and test sets, and the model predicted the 1st, 5th, 6th, 7th, 12th, and 12th samples. The model predicted the purchase intention of the 1st, 5th, 6th, 7th, 12th and 13th customers.

Interest Rate Competition among C Banks, S Banks, and Credit Unions

Compared to C corporation banks, S corporation banks and credit unions are considered tax-exempt institutions, with credit unions receiving the greatest tax benefit. Institutions can choose to pass this benefit onto customers in the form of higher deposit rates and lower loan rates. We test this hypothesis by analyzing a dataset of 11 distinct deposit and loan products over 15 years and compare interest rates offered by depository institutions partitioned into three size groups. Overall, our results indicate credit unions offer higher deposit and lower loan rates compared to commercial banks, particularly for timed deposits (CDs), money market accounts, auto loans, and fixed unsecured loans. In contrast, S banks and C banks offer similar interest rates on most deposit and loan products. These findings generally hold across all size groups and differences become more pronounced when comparing larger credit unions to commercial banks. Notably, credit unions in the large size group offer better interest rates to customers for all products analyzed.

Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model

As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect.

Cars, Debt, and Carcerality

Consumer lore in the United States celebrates the automobile as a “freedom machine,” consecrating the mobility of a free people. Yet, paradoxically, the car also functions at the crossroads of two great systems of unfreedom and immobility—the debt economy and the carceral state. Drawing on interviews with formerly incarcerated people, this article investigates this paradox in detail, tracing how the long arms of carcerality and debt operate in tandem in the daily life of car use and ownership. It describes the ways in which credit dovetails with capture—pretextual traffic stops, revenue policing from fines and fees, the overreach of automobile-related surveillance, the predatory auto loan and repossession businesses, and criminal justice debt—all shot through with profound racial bias. In the autocentric United States, transportation is a basic need, yet it has never been recognized or funded as a public good. As the “age of mobility” beckons, with autonomous driving as its technological centerpiece, the authors call for the social liberation of the automobile. From the outset, the automobile has traded on the romance of the open road, but it has too long served as a vehicle of inequality and injustice.

Racial Disparities in the Auto Loan Market

Abstract We document racial disparities in auto lending. Combining credit bureau records with borrower characteristics, we find that Black and Hispanic applicants’ approval rates are 1.5 percentage points lower, even after controlling for creditworthiness. In aggregate, this effect crowds out 80,000 minority loans each year. Results are stronger where racial biases are more prevalent and lending competition is lower. Minority borrowers pay 70-basis-point higher interest rates, but default less ceteris paribus, consistent with racial bias rather than statistical discrimination. A major antidiscrimination enforcement policy initiated in 2013, but halted in 2018, reduced unexplained racial differences in interest rates by 60$\%$. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Selected Issues In The Taxation Of Swaps, Structured Finance and Other Financial Products

This article is based on a paper presented by the author to the Tax Review of New York City on November 16, 1992.The last decade has seen an explosive growth in the use of swaps and other derivative products as part of the asset and liability management strategies of corporations and other institutional investors. Beginning with interest rate and currency swaps, the menu of swap products has expanded to include commodity swaps, equity index swaps, equity swaps, total return swaps, basis swaps, and interest rate caps, floors and collars. According to industry sources, the market for interest rate swaps has grown in notional principal amount from an estimated $3 billion in 1982 to over $3.065 trillion by the end of 1991 (the most recent figures available). Similarly, the outstanding aggregate notional principal amount of currency swaps has grown from $182 billion in 1987 to $807 billion by the end of 1991.At the same time as the swap market has been expanding, the structured finance market, including mortgage and asset securitization, has seen similar growth. Beginning in the 1970s, U.S. government-backed enterprises such as the Government National Mortgage Association ("Ginnie Mae") and the Federal Home Loan Mortgage Corporation ("Freddie Mac") sponsored transactions in which pools of residential first mortgages were bundled into pass-through certificates that were then sold to thrifts, banks and other institutional investors. In 1983, Freddie Mac introduced collateralized mortgage obligations ("CMOs"). CMOs, which divided up the cashflows from an underlying pool of mortgages into separate debt securities having different maturity, interest rate and credit characteristics, dramatically broadened the market for mortgage-backed securities. With the enactment of the "Real Estate Mortgage Investment Conduit" ("REMIC") provisions as part of the 1986 Tax Reform Act, the taxation of mortgage pass-through and pay-through securities became subject to a new, self-contained set of rules. Moreover, since the mid-1980s, structured finance techniques originally employed in securitizing mortgages have been applied to a broad range of other financial assets, including stocks, credit card receivables, auto loans, lease receivables, home equity loans, commercial and industrial loans and trade receivables.

The Effects of Mortgage Credit Availability: Evidence from Minimum Credit Score Lending Rules

This paper uses changes in mortgage lenders’ minimum credit score thresholds to credibly identify the effects of access to household credit. Falling under these thresholds has very large negative effects on borrowing for up to two years, and these effects fail to reverse within four years. The effects are particularly concentrated among individuals who have relatively high credit demand and face relatively large contractions in credit supply. In addition, access to new mortgage credit reduces delinquency on nonmortgage debt and appears to spill over to demand for auto loans. (JEL G21, G51, R21)

Auto ABS Loan-Level Data: Improved Risk Analysis

In 2014, the US Securities and Exchange Commission’s Regulation AB mandated loan-level data disclosure for public auto loan asset-backed securities (ABS). As a result, the loan level data from 2017 to 2021 display a rich set of loan-level variables that shed light on collateral performance patterns and improve risk analysis, especially for credit risk. Statistical predictive models that incorporate these loan-level drivers substantially improve the accuracy and granularity of default forecasts for auto ABS loans, and can provide benefits for investors and risk managers who use them.

Risks of long-term auto loans

Long-term auto loans have become more popular in the last decade, and most auto loans originated after 2013 have terms longer than five years. We find that the choice of long-term auto loans is associated with larger origination amounts, and borrowers of long-term auto loans tend to be more credit constrained. After controlling for borrower risk factors available from credit bureau data and macroeconomic conditions, we find that auto loans with terms beyond five years have higher delinquency rates than shorter-term loans during each year in their lifetimes. We further find that the yield curves of auto loans are inverted, and the interest rates for auto loans of six or more years have increasingly become lower than those of shorter-term auto loans.

Credit Risk Contagion in German Auto Loans

Credit Risk Contagion in German Auto Loans

Using Asymmetric Dominance to Resolve Toxic Debt: Combining Online and Field Experiments

This study employs a behavioral technique known as asymmetric dominance used to nudge defaulted borrowers toward a repayment option that is in their best interest (lowest implied APR) while at the same time encouraging greater repayment, resulting in a win-win for both lenders and borrowers. We demonstrate the efficacy of this approach through an online experiment and then through a field experiment of 1st- and 2nd-lien actual defaulted mortgage pools. We address the generalization of asymmetric dominance by documenting success across multiple consumer asset classes – mortgages, auto loans, payday loans, student loans, health care debt and credit card debt. Finally, our results hold across 2-, 3-, and 4-digit monthly repayment amounts.

Analyzing the Aftermath of PSB Micro Service Portal Implementation in India

‘PSB Portal’ is a micro-service digital platform which primarily connects funding needs of Micro, Small and Medium Enterprises (MSMEs) to Banks for business. It is a strategic initiative of SIBDI with five other Public Sector Banks and was launched by Government of India, in November 2018, to provide loans to MSMEs of up to Rs.1 crore. The main motive of this initiative is to ease the process of fund availability to MSMEs and decrease the turnaround time of credit delivery to MSME sector. The new ‘PSB portal’ also makes it possible to provide timely finance at reasonable and competitive rates of interest to MSME sector. MSME sector forms a vital component of the economy in terms of their contribution to employment generation, exports and inclusive growth. One-third of the country's GDP is contributed by more than 50 million MSMEs in India. Thus, the role of MSMEs in the economic and social development of the country is not to be over-looked. There is need for finance that meets the life-cycle needs of MSMEs with a strong oversight mechanism in Banks to see that MSME-related lending policies are implemented at the operating level, is high. Such measures by the Government would serve the purpose of ease of access to funding requirements of MSME’s and also help open new avenues for Banks to increase their business while tapping new customers thereby increasing their priority sector assets. It will make formal banking more inclusive and deepen the roots of financial inclusion via digitization in the economy. After almost after a year of this initiative, on September 5 2019, Retail products (Home Loan Personal Loan and Auto Loan) were also added to the portal to increase the ease of credit to retail customers to further facilitate purchasing power in the economy. This paper will analyse the challenges faced by the Bankers while financing MSMEs via PSB portal and the reasons for rejection of the applications with further prospects of the scheme.

Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques

Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques

A STUDY ON FINANCIAL ANALYSIS AND PERFORMANCE OF HDFC BANK

This study has been carried out to evaluate the nancial performance of HDFC bank. HDFC was amongst the rst to receive an 'in principle' approval from the Reserve Bank of India (RBI) to set up a bank in the private sector. HDFC Bank is the second largest Private sector Bank in India and one of the top 5 banks in the country. The bank has a network of 5000+branches and 16000+ATMs in 2902 cities/towns. HDFC Bank provides a number of products and services including wholesale banking and retail banking, treasury, auto loans, two wheeler loans, personal loans against property, consumer durable loans, life style loan, credit cards and the various digital products. The nancial performance of above mentioned bank has been evaluated for the past ve years i.e. 2017, 2018, 2019, 2020, 2021. The data analyzed by ratio analysis such as current ratio, liquid ratio, xed assets ratio, debt-equity ratio and net prot ratio and give interpretation to each ratio. To conclude this nancial soundness of the bank is satisfactory during the study period.

Meta Dynamic Pricing: Transfer Learning Across Experiments

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation in which the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon T) for N different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior (meta-exploration) with the need to leverage the estimated prior to achieve good performance (meta-exploitation) and (ii) accounting for uncertainty in the estimated prior by appropriately “widening” the estimated prior as a function of its estimation error. We introduce a novel prior alignment technique to analyze the regret of Thompson sampling with a misspecified prior, which may be of independent interest. Unlike prior-independent approaches, our algorithm’s meta regret grows sublinearly in N, demonstrating that the price of an unknown prior in Thompson sampling can be negligible in experiment-rich environments (large N). Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared with prior-independent algorithms. This paper was accepted by George J. Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.

Competition and Selection in Credit Markets

We present both theory and evidence that increased competition may decrease rather than increase consumer welfare in subprime credit markets. We present a model of lending markets with imperfect competition, adverse selection and costly lender screening. In more competitive markets, lenders have lower market shares, and thus lower incentives to monitor borrowers. Thus, when markets are competitive, all lenders face a riskier pool of borrowers, which can lead interest rates to be higher, and consumer welfare to be lower. We provide evidence for the model’s predictions in the auto loan market using administrative credit panel data.

Economic Education and Household Financial Outcomes during the Financial Crisis

Using cross-sectional data from a nation-wide survey of American head-of-households conducted in the spring of 2010, we examined the ameliorating effects of economic literacy on the probability of specific household financial outcomes resulting from the 2008 financial crisis and the associated Great Recession. A series of probit regressions were estimated to capture the impact of economic literacy on the probability that households experienced job loss, delinquent mortgage payments, delinquent credit card payments, delinquent auto loan payments, loss of home, and personal bankruptcy. The head-of-household’s economic literacy was measured by the level of formal education received in economics and by the score achieved on an in-survey quiz of basic economic concepts and principles. The results indicate that realized quiz scores were correlated with the mitigation of job loss, late payment behavior, and personal bankruptcy, ceteris paribus. However, the results for the impact of formal economic coursework in school were mixed.